Intelligent Agents And Systems In Action

We can define intelligent agents and systems as structures that are able to make use of different types of sensors for perceiving their environmental settings and reacting to stimuli with the help of effectors. We can compare intelligent systems to human systems that have the power of coordination and communication with surroundings. The prime goal of an intelligent system is to identify the surrounding environment and the various objects and coordinate both motion and cognition to achieve an end task.

In the age of artificial intelligence, automated systems are proliferating and becoming an important part of various research studies. In addition to this, these systems are also becoming the center of various types of artificial intelligence courses.

An ideal rational agent

The rationality of an intelligent agent depends upon four prime factors. The first factor is the measurement of performance of an intelligent system that gives an account of the accuracy with which it performs a specific task. The second important factor is the execution sequence of various events that an intelligent system needs to perform simultaneously. Given the multiplicity of various events, an ideally rational agent should be able to form a sequence of execution and perform various tasks flawlessly. The third important factor is the information collection capacity of an ideally rational agent about intimate surroundings. Finally, the fourth important factor that determines the rational behavior of intelligent agents is the seamless communication with other agents and systems.

Levels of autonomy of intelligent agents

The intelligent agents and systems that we see around us in the present times are built-in with a significant knowledge base. With the help of this built-in knowledge base, an agent performs different types of actions to achieve specific tasks. Needless to mention, there are a large number of ways through which a specific task can be performed. An intelligent agent is free to choose the course of action to achieve a particular task. This means that an intelligent agent and a corresponding intelligent system is given a level of autonomy to perform a course of action. The level of autonomy that we provide to an intelligent system determines the learning capacities and the cognitive abilities of an intelligent system. When the behavior of an intelligent system is determined by its knowledge base as well as its experience, we call such a system an autonomous system.

Intelligent agents in action

It is important to understand and exemplify various types of intelligent agents, their action systems as well as their corresponding environment. We choose five types of environments and describe the agent type as well as its corresponding actions and goals. The first environment that we describe is a hospital and the agent type is the medical diagnosis system. The aim of the agent is to examine various types of symptoms in a patient by comprehending his medical history. The goal is to enable real-time analytics of various diagnostic tests and decrease the time of recovery. The second environment that we consider is the database of images obtained from a satellite. The agent type is called the image processing system and its aim is to analyze various types of images on the basis of their color and intensity. The goal is to categorize these images and derive information from them that can be transferred to respective departments. The third environment that we consider is the manufacturing unit where the agent type deployed is a picking robot. The aim of the robot is to pick up and arrange various types of objects in respective packing clusters. The fourth working environment that we describe is a refinery. In this environment,  the aim of the agent is to detect different types of temperature and pressure readings and sound an alarm when abnormal readings are encountered. This is extremely important for safety purposes. The last environment that we describe is an automated learning environment. In this environment, we deploy an interactive learning chatbot that interacts with students and understands their problems by administering a set of questions. The aim of this exercise is to improve the scores of students on various tests.

Concluding remarks

The application of intelligent agents and systems is rapidly growing in the age of automation. Not only are these intelligent systems undergoing transformation to cater to user requirements but are also gradually adapting to different environments and circumstances. In the future, such agents and systems may acquire deep cognitive capabilities and much more responsive components.


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